Longitudinal cohort study of injury type, settings, treatment and costs in British Columbia youth, 2003–2013
Bibliographic record
Abstract
BACKGROUND: In 2010 in British Columbia (BC), Canada, total injury costs per capita were higher among youth aged 15-24 years than in any other age group. Injury prevention efforts have targeted injuries with high mortality (transportation injuries) or morbidity (concussions). However, the profile and health costs of common youth injuries (types, locations, treatment choices and prevention strategies) and how these change from adolescence to young adulthood is not known. METHODS: Participants (n=662) were a randomly recruited cohort of BC youth, aged 12-18, in 2003. They were followed biennially across a decade (six assessments). RESULTS: Serious injuries (defined as serious enough to limit normal daily activities) in the last year were reported by 27%-41% of participants at each assessment. Most common injuries were sprains or strains, broken bones, cuts, punctures or animal bites, and severe bruises. Most occurred when playing a sport or from falling. Estimated total direct cost of treatment per injury was approximately $2500. In addition, 25% experienced serious injuries at three or more assessments, indicating possible differences that warrents further investigation. CONCLUSIONS: The occurence and health cost of common injuries to youth and young adults are underestimated in this study but are nevertheless substantial. Ongoing surveillence, awareness raising, and prevention efforts may be needed to reduce these costs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".